active and passive microwave remote sensing of precipitation at high latitudes n.
Skip this Video
Loading SlideShow in 5 Seconds..
Active and passive microwave remote sensing of precipitation at high latitudes PowerPoint Presentation
Download Presentation
Active and passive microwave remote sensing of precipitation at high latitudes

Loading in 2 Seconds...

  share
play fullscreen
1 / 34
Download Presentation

Active and passive microwave remote sensing of precipitation at high latitudes - PowerPoint PPT Presentation

knut
154 Views
Download Presentation

Active and passive microwave remote sensing of precipitation at high latitudes

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Active and passive microwave remote sensing of precipitation at high latitudes R. Bennartz - M. Kulie - C. O’Dell (1) S. Pinori – A. Mugnai (2) (1) University of Wisconsin – AOS – Madison,WI - USA (2)Institute of Atmospheric Science and Climate, National Research Council, Rome, Italy

  2. Outline • Introduction • High latitudes and why study light rain snow • Modeling Strategy • Light snow/rain validation database • Case study • Light snowfall event from radar • Satellite-model comparison • UW-NMS mesoscale model comparison • Sensitivity of the MW frequencies to perturbation in the IWC • Outlook • Towards GPM • IPWG

  3. SNOW AT MID-TO-HIGH LATITUDES (Figures from P. Yoe, J. Koistinen) Snowfall Accumulation Snow to Total Precipitation Ratio At mid-to-high latitudes, snowfall represents a substantial portion of the precipitation. From higher latitudes at least 90% of the precipitation occurs at rates less than 3 mm/hr and 60 % at less than 1 mm/h

  4. What we can observe Radar reflectivity (vertically resolved) Passive MW brightness temperatures (vertical integral)

  5. What we can NOT observe: Drop size distribution Ice particle density Index of refraction . . .

  6. What we can NOT observe: Drop size distribution Ice particle density Index of refraction . . . We need models to relate the microphysics to microwave optical properties

  7. What we can NOT observe: Drop size distribution Ice particle density Index of refraction . . . We need models to relate the microphysics to microwave optical properties And those models have to agree with all available information

  8. How can we trust our modeling assumptions?

  9. Radar reflectivites Environmental data How can we trust our modeling assumptions? Precip microphysics model Change microphysics Radiative transfer model Observed TBs Compare Simulated TBs

  10. One Microphysics Model (Bennartz & Petty 2001) Adjustable parameters: Ice densitySize of ice relative to liquid particles Consistent description of Radar Refl/ Fall Speed/ Particle number concentration X = 0.5 X = 1 X = 2 Frozen Liquid

  11. High latitude light snow/rain database (2002-ongoing) Radar data BALTRAD radar composites BALTRAD gauge adjustments Gotland radar volume scans Satellite data NOAA 15,16,17 AMSU-A/B AQUA AMSR-E SSMIS (if/when available) Global/regional model data: global NCEP/GFS data UW-NMS model (for selected cases)

  12. MODIS 15 March 2003 CASE STUDY Light snowfall over the Baltic Sea the 12-13 January, 2003. Comparing different ground-based, satellite and modelling data

  13. 2003-01-12 0130 UTC Gotland radar reflectivity (lowest scan)

  14. 2003-01-12 0130 UTC

  15. 2003-01-12 0130 UTC

  16. 2003-01-12 0130 UTC Radar composite (gauge adjusted surface rain rate)

  17. 2003-01-12 0130 UTC AMSU 89 GHz and 150 GHz NOAA-17 0107 UTC

  18. 2003-01-12 0130 UTC AMSU 89 - 150 GHz NOAA-17 0107 UTC

  19. 2003-01-12 0130 UTC AMSR 89 GHz AQUA 01:31 UTC

  20. RT :Reverse 3D Monte-Carlo with Henyey-Greenstein Phase Function, on a 2 km x 2 km x 1 km grid with 10 vertical levels. FASTEM-2 Ocean emissivity model, everywhere. 89 GHz (a) channel, at radar resolution 89 GHz (a) channel, at 36 GHz resolution

  21. Model vs. Observation Comparison: Little bias, reasonably good correlation. Only areas where there is precip

  22. UW-NMS MODEL SETUP 3 two-way nested grids 18 hr simulation: from 12 UTC 11 January to 06 UTC 12 January 2003 3rd grid: 6 hours from 00UTC 12 Jan 6 category bulk microphysics: Cloud droplets, Rain, Pristine crystals, Snow (rimed crystals/low density graupel), Aggregated crystals, High density graupel Mixing ratios of total water and 5 hydrometeors categories are predicted: rain, graupel, snow, pristine crystals, and aggregates. Cloud water is diagnosed [Tripoli 1992]

  23. RADAR-MODEL COMPARISON Selected two areas of similar environmental parameters (LWP,WVP). Take into account the radar beam width at ~100 km from the radar site dBZ

  24. SCATTERING INDEX FOR PRECIPITATING AREA Relation between scattering index and 89 GHz brightness temperature for model (blue) and AMSR (red) for x=1; Relation between scattering index and 89 GHz brightness temperature for radar (red) and AMSR (black) for x=1. Red: radar Black:satellite Radar and model datasets are in good agreement, with the scattering index ranging from -5 and 20 K.

  25. AMSU–MODEL COMPARISON Relation between TB89-TB150 and the surface precipitation for different size ratio x for observed AMSU-B data (red) and simulated data (blue). X=1

  26. Where are we? Microphysics model agrees with radar observations Microphysics model agrees with passive mw observation at various scattering frequencies Surface rain rates are comparable to gauge-adjusted radar

  27. Channel definition for new sensors The Jacobian is defined as the partial derivative of a function: The increase the IWC of ε allow us to see the sensitivity of TBs to perturbations in hydrometeor contents.

  28. 150 GHz 89 GHz K / (g/m3) K / (g/m3) • 150 GHz is more sensitive to the IWC perturbation than the 89GHz especially in the upper levels.

  29. 118±8.5 GHz 118±4.2 GHz 118±2.3 GHz K / (g/m3) Potential of the O2-sounding channels for frozen precipitation detection

  30. Conclusions/Outlook • Use all observable Tb dBZ to ensure consistency of microphysical assumptions in observation space • Need for coordination of different groups working towards snowfall/high lat precip. using different microphysics schemes (intercomparison) -> IPWG

  31. Conclusions/Outlook • Use all observable Tb dBZ to ensure consistency of microphysical assumptions in observation space • Need for coordination of different groups working towards snowfall/high lat precip. using different microphysics schemes (intercomparison) -> IPWG • Dedicated experiments necessary to better understand cloud microphysics

  32. Conclusions/Outlook • Use all observable Tb dBZ to ensure consistency of microphysical assumptions in observation space • Need for coordination of different groups working towards snowfall/high lat precip. using different microphysics schemes (intercomparison) -> IPWG • Dedicated experiments necessary to better understand cloud microphysics • BUT on a global scale we have to go with simple solutions for retrieval algorithms etc…

  33. Two more things for high latitudes • We need channels that are surface blind • We need GPM like radars

  34. Thanks